roughly classify the related algorithms into two categories. One category includes traditional algorithms

frameworks and proposing corresponding algorithms, to optimally schedule virtualized elements under different

; deterministic, dynamic, stochastic, robust and combinatorial optimization models; solution algorithms, learning

algorithms as applied to planning and scheduling problems. While it is not possible to give a comprehensive

scheduling policies. To obtain optimal and near optimal scheduling policies new models and algorithms are

, hesitant fuzzy clustering algorithms and hesitant fuzzy multi-attribute decision making methods. Since its

emphasizes essential foundations throughout, rather than providing a compendium of algorithms and theorems

computational intelligence algorithms. Many solved example problems are included that range from simple to quite

develops the theory of optimal search for a moving target, providing algorithms for computing optimal plans

features free C programs to implement the major algorithms covered, including the two-phase simplex method

covered include algorithms for solving convex, network, mixed-integer, nonlinear, and global optimization

algorithms) · Detailed coverage of the Bellman equation framework for Markov Decision Processes (MDPs), along